Systems and methods for interactive annuity product services using machine learning modeling

ABSTRACT

A server computing device generates an input data set by determining a set of user information, a set of market index information, and available annuity products. A machine learning processor executes a price optimization module to traverse a computer-generated annuity matching model and select a subset of the available annuity products that are associated with product characteristics that match user objectives and generate annuity product recommendations for the user. The processor executes a market simulation module to traverse a computer-generated annuity performance prediction model using the annuity product recommendations and predictions of market performance to generate simulated outcomes for each of the annuity products. A client device generates a graphical user interface for display to the user via a display device, the graphical user interface including visual representations of each of: the annuity product recommendations and the simulated outcomes.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/264,163, filed on Dec. 7, 2015, the entirety of which isincorporated herein by reference.

TECHNICAL FIELD

This application relates generally to methods and apparatuses, includingcomputer program products, for interactive annuity product servicesusing machine learning modeling.

BACKGROUND

The life insurance industry offers retirement products that allow forasset accumulation, in the form of deferred annuities. Deferredannuities are typically designed by the industry to have an accumulationphase followed by an optional annuitization phase. In a typical annuitypurchase, a policyholder makes an upfront payment (sometimes a series ofupfront payments) to the insurance company, which is tracked as thepolicyholder's account value. During the accumulation phase credits aremade to the account value and these credits are not subject to currenttaxation.

In the case of a Fixed Guaranteed Annuity (GA), the policyholder'saccount value is credited at a fixed rate of return. The account valueis guaranteed by the insurance company. In the case of a Fixed IndexAnnuity (FIA), the policyholder's account value is credited at a ratethat is a function of changes in a market index selected by thepolicyholder, such as the S&P 500, over a particular period of time. Aminimum annual rate of return, usually 0.0%, is guaranteed by theinsurance company. In the case of a Variable Annuity (VA), thepolicyholder's account value is determined by the value of investmentschosen by the policyholder and held by the insurance company in one ormore sub-account. The insurance company does not guarantee any minimumaccount value or rate of return.

The state of the art of the annuity market is lacking, as will bediscussed further herein, as current products have deficiencies such asbeing:

-   -   Inflexible;    -   Expensive (fee-ridden);    -   Opaque;    -   Limited in upside (accumulation) potential.        Fixed Index Annuity

Although credit rates for FIA products do vary with changes in areference market index, the extent of this variation is significantlylimited. For instance, an insurance company will typically set a cap onannual credited rates. These caps are set at very low levels relative tothe potential returns of the market index, for example at 3.0%. In thisexample, the policyholder would earn a credited rate of between 0.0% and3.0% in a given year, depending on the change in the market index. Thisis clearly a de minimis level of “index participation” and does notprovide a material level of “market upside” potential (when compared toS&P 500 long term average annual returns of 8-10%). Furthermore, whencompared to fixed rates of return offered by insurance companies on GA(1.5-2.5% guaranteed), there is limited incremental accumulationpossibility available to policyholders buying FIA.

Additionally, since FIA credited rates are based on one-year marketindex changes, insurance companies selling FIAs have to engage in costlyannual hedging (trading in market index derivatives) related to theseproducts. Maintaining the hedges exposes the insurance company to incomestatement volatility, as accounting rules require that the hedgepositions be marked to market. It is inefficient for insurance companiesto suffer from this financial reporting volatility for a product thatdoes not offer significant value to policyholders. Insurance companyaccounting forces insurance companies to value their hedge portfolioassets in a manner distinct from the valuation of the relatedliabilities, leading to increased and unnecessary volatility ofinsurance company surplus.

Fixed Guaranteed Annuity

In GA products, policyholders do not have any upside or equityparticipation potential. They are unable to allocate portions ofaccumulated gains to riskier strategies in order to create thepossibility for higher returns. These options in GA products are simplynot allowed by the applicable laws.

Variable Annuity

VA products do allow policyholders to take on additional risk inexchange for additional return potential, however VA products havenumerous drawbacks, including confusing and unclear fee structures. VAproducts expose policyholders to downside risk unless policyholderspurchase additional protections from the insurance company at anadditional cost. These protections are rigid (i.e., cannot be easilycustomized to suit objectives) and are subject to opaque fees.

Deficiencies of Existing Methodologies and Systems

Current art annuities fail to provide transparent products that allowpolicyholders to customize and change risk profile as their needs evolveover different life stages, while also providing meaningful potentialfor accumulation and upside. Current art annuities have built-in riskmanagement features that purport to protect policyholders. However,these features cannot be chosen or modified by policyholders.Furthermore, the cost of these features is not transparent and cannot bedetermined explicitly by the policyholder.

In current art annuities, existing annuities are administered in a rigidmanner and a policyholder's options to modify either their risk exposureor the fees associated with their product are virtually non-existent.There is a fixed set of products and a small set of riders that areoffered and each are priced separately with pre-set pricing.Policyholders are locked into longer term inflexible and immutablecontracts, even though it is likely that their circumstances and needswill change during the life of the annuity contract. The main optionavailable to policyholders who need to make a change is early surrenderof their annuities. Early surrender leads to costly penalties and thuscauses immediate losses to policyholders. In addition, any alternativeproduct that the policyholder chooses as a replacement for the originalannuity will be similarly inflexible and suboptimal.

Furthermore, existing computing systems—even systems with advancedprocessing capabilities—that handle functions such as user-facing needsassessment, price optimization, market performance and portfoliosimulation in the context of annuity management typically match users ina linear fashion to existing pre-configured products. Such systems donot leverage more sophisticated software-based data processingtechniques that can only be performed by specialized computers, such asmachine learning and artificial intelligence.

SUMMARY

The subject disclosure relates to systems for creating and offeringannuities that are a hybrid between today's fixed index annuity andtoday's variable annuity. This offering provides for an annuity with awide array of use-selectable investment choices (in index form) alongwith a flexible risk management design that enables the policyholder tooptimize objectives for the annuity in a straightforward design that canchange over time as the policyholder's risk appetite changes.

The new product is preferably a fixed index annuity that offers:

-   -   1. Access to market-leading investment strategies delivered as a        menu of indexing alternatives;    -   2. Lifecycle Risk Management: the versatility to select the        precise level of risk and to change the selection over time;    -   3. Transparent fee design where policyholders know what they are        paying for and only pay for the risk management options that        they desire.

The present system relates to improvements to existing systems foroffering, managing, and administering annuities. Annuity companies havefor many years implemented computer systems (which are sometimesnetworked with internal and external systems) that manage and administerannuities which were issued to its customers (the policyholders). Thesesystems can include complex software components that are implemented oncomputer hardware (and networks) to provide the management andadministration of annuities. Software that configures the hardware isstored in non-volatile memory such as a hard drive or other memorydevice to operate the system. These existing systems, as explainedabove, will include features for managing risk, individual annuityaccounts, financial payments, and investments by the annuity company insupport of its annuity obligations and related risks. According toembodiments of the present invention, these existing systems aremodified or a new system is implemented that applies software, hardware,and related data to effectuate the new annuity design (and relatedvariations) that are described herein. The following sections firstdescribe a particular annuity design that is provided by the system andnext provide additional discussion about the system, features of thesystem, and overall considerations. Thereafter, an initial claim sectionis provided but it is to be understood that other claims are evident andunderstood from the present description.

In addition, the machine learning techniques described herein providethe advantage of generating a large set of potential permutations ofproduct features that are tailor-made to a specific customer. Thetechniques provide a robust process of probing the needs of a customerto fully understand their objectives, risk tolerances, fee constraints,and other factors, independent of any product recommendation. Thetechniques then leverage a product/price optimization phase thatconsiders every possible permutation of product attributes to arrive ata few recommendations that are both feasible (i.e., the insurancecompany can issue on those terms) and which maximize compatibility witha user needs profile. It should be understood that the techniquesadvantageously enable pricing to be performed non-linearly ‘on the fly’(e.g., more complex than simply adding up a few rider fees).

The invention, in one aspect, features a system comprising a servercomputing device communicably coupled to a database computing device andhaving a machine learning processor. The server computing device isprogrammed to generate an input data set by: determining a set of userinformation associated with a user, including user demographics, userrisk preferences, and user objectives; determining a set of market indexinformation, including historical market index information, currentmarket index information, and forecast market index information; anddetermining one or more available annuity products using the set of userinformation. The machine learning processor is configured to execute aprice optimization module to traverse a computer-generated annuitymatching model using the input data set to select a subset of theavailable annuity products that are associated with productcharacteristics that match one or more of the user objectives andgenerate one or more annuity product recommendations for the user basedupon the subset of annuity products. The machine learning processor isconfigured to execute a market simulation module to traverse acomputer-generated annuity performance prediction model using the subsetof annuity products selected by the price optimization module and one ormore predictions of market performance, the market simulation enginegenerating one or more simulated outcomes for each of the annuityproducts in the subset of annuity products. The system further comprisesa client computing device communicably coupled to the server computingdevice. The client computing device is configured to generate agraphical user interface for display to the user via a display device,the graphical user interface including one or more visualrepresentations of each of: the one or more annuity productrecommendations and the one or more simulated outcomes.

The invention, in another aspect, features a method. A server computingdevice communicably coupled to a database computing device and having amachine learning processor generates an input data set by: determining aset of user information associated with a user, including userdemographics, user risk preferences, and user objectives; determining aset of market index information, including historical market indexinformation, current market index information, and forecast market indexinformation; and determining one or more available annuity productsusing the set of user information. The machine learning processorexecutes a price optimization module to traverse a computer-generatedannuity matching model using the input data set to select a subset ofthe available annuity products that are associated with productcharacteristics that match one or more of the user objectives andgenerate one or more annuity product recommendations for the user basedupon the subset of annuity products. The machine learning processorexecutes a market simulation module to traverse a computer-generatedannuity performance prediction model using the subset of annuityproducts selected by the price optimization module and one or morepredictions of market performance, the market simulation enginegenerating one or more simulated outcomes for each of the annuityproducts in the subset of annuity products. A client computing devicecommunicably coupled to the server computing device generates agraphical user interface for display to the user via a display device,the graphical user interface including one or more visualrepresentations of each of: the one or more annuity productrecommendations and the one or more simulated outcomes.

Any of the above aspects can include one or more of the followingfeatures. In some embodiments, the input data set is generated byretrieving at least a portion of the user information, market indexinformation, and available annuity products from an external datasource. In some embodiments, the server computing device is furtherconfigured to receive at least a portion of the user information fromthe user. In some embodiments, the server computing device is furtherconfigured to execute, by the machine learning processor, a user needsassessment module to traverse a computer-generated user insight analysismodel using the user information, the user needs assessment modulegenerating one or more expected user attributes. In some embodiments,the server computing device is further configured to transmit the one ormore expected user attributes to the client computing device for displayto the user. In some embodiments, the server computing device is furtherconfigured to receive a response to the one or more expected userattributes from the client computing device and re-execute the userneeds assessment module using the received response to adjust one ormore of the expected user attributes.

In some embodiments, the graphical user interface includes one or moreinput controls to enable interaction with and manipulation of the one ormore visual representations by the user of the client computing device.In some embodiments, server computing device is further configured tobuild, by the machine learning processor, the computer-generated annuitymatching model by training a machine learning algorithm programmed onthe machine learning processor against a training data set. In someembodiments, the server computing device is further configured to build,by the machine learning processor, the computer-generated annuityprediction performance model by training a machine learning algorithmprogrammed on the machine learning processor using the input data set.

In some embodiments, the product characteristics are reference marketindices, participation rate, downside protection, commitment term,income benefits, and fees. In some embodiments, the user riskpreferences include a risk tolerance. In some embodiments, the machinelearning processor returns the generated one or more annuity productrecommendations to the price optimization module as input to update thecomputer-generated annuity matching model.

In some embodiments, the machine learning processor returns thegenerated one or more simulated outcomes to the market simulation moduleas input to update the computer-generated annuity performance predictionmodel. In some embodiments, the one or more predictions of marketperformance include at least one prediction of market performancereceived from the client computing device as input by the user. In someembodiments, the one or more predictions of market performancecorrespond to a performance of one or more market indices. In someembodiments, the one or more simulated outcomes for each of the annuityproducts correspond to an expected rate of return for the annuityproduct.

Other aspects and advantages of the invention will become apparent fromthe following detailed description, taken in conjunction with theaccompanying drawings, illustrating the principles of the invention byway of example only.

BRIEF DESCRIPTION OF THE DRAWINGS

The advantages of the invention described above, together with furtheradvantages, may be better understood by referring to the followingdescription taken in conjunction with the accompanying drawings. Thedrawings are not necessarily to scale, emphasis instead generally beingplaced upon illustrating the principles of the invention.

FIG. 1 is a block diagram of a system used in a computing environmentfor generating annuity product recommendations and risk strategies usinga machine learning processor.

FIG. 2 is a flow diagram of a method for generating annuity productrecommendations and risk strategies using a machine learning processor.

FIG. 3 is a workflow diagram of a method for generating user-specificinsight data using a machine learning processor executing a user needsassessment module.

FIG. 4 is a workflow diagram of a method for generating user-specificproduct recommendations using a machine learning processor executing aprice optimization module.

FIG. 5 is a workflow diagram of a method for generating user-specificand product-specific simulated outcomes for annuity productrecommendations.

FIG. 6 depicts an exemplary graphical user interface for the purposes ofcreating a user account and user profile.

FIG. 7 depicts an exemplary graphical user interface for the purposes ofcollecting user needs assessment input.

FIG. 8 depicts an exemplary graphical user interface for the purposes ofpresenting user insights and collecting user feedback on the insights.

FIG. 9 depicts an exemplary graphical user interface for the purposes ofdisplaying annuity product recommendations.

FIG. 10 depicts an exemplary graphical user interface for the purposesof displaying a simulated outcome for a particular annuity productrecommendation.

FIG. 11 is a workflow diagram of a method for generating a user-specificand product-specific risk profile using a machine learning processorexecuting a risk and hedging strategy module.

FIG. 12 is a workflow diagram of a method for generating hedgestrategies using a machine learning processor executing a risk andhedging strategy module.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a system 100 used in a computingenvironment for generating annuity product recommendations and riskstrategies using a machine learning processor (e.g., processor 112). Thesystem 100 includes a client computing device 102, a communicationsnetwork 104, a database 106, a server computing device 108 having aspecialized machine learning processor 112 that executes a user needsassessment module 109, a price optimization module 110, a marketsimulation module 111, a risk and hedging strategy module 113, and oneor more data sources 114.

The client computing device 102 connects to the communications network104 in order to communicate with the server computing device 106 toprovide input and receive output relating to the process of generatingannuity product recommendations and risk strategies using a machinelearning processor as described herein. For example, client computingdevice 102 can be coupled to a display device that presents a detailedgraphical user interface (GUI) with output resulting from the analysismethods and systems described herein, where the GUI is utilized by anoperator to review the output generated by the system. In addition, theclient computing device 102 can be coupled to one or more input devicesthat enable an operator of the client device to provide input to theother components of the system for the purposes described herein.

Exemplary client devices 102 include but are not limited to desktopcomputers, laptop computers, tablets, mobile devices, smartphones, andinternet appliances. It should be appreciated that other types ofcomputing devices that are capable of connecting to the components ofthe system 100 can be used without departing from the scope ofinvention. Although FIG. 1 depicts a single client device 102, it shouldbe appreciated that the system 100 can include any number of clientdevices. And as mentioned above, in some embodiments the client device102 also includes a display for receiving data from the server computingdevice 108 and displaying the data to a user of the client device 102.

The communication network 104 enables the other components of the system100 to communicate with each other in order to perform the process ofgenerating annuity product recommendations and risk strategies using amachine learning processor as described herein. The network 104 may be alocal network, such as a LAN, or a wide area network, such as theInternet and/or a cellular network. In some embodiments, the network 104is comprised of several discrete networks and/or sub-networks (e.g.,cellular to Internet) that enable the components of the system 100 tocommunicate with each other.

The database 106 is a computing device (or in some embodiments, a set ofcomputing devices) that is coupled to the server computing device 108and is configured to receive, generate, and store specific segments ofdata relating to the process of generating annuity productrecommendations and risk strategies using a machine learning processoras described herein. In some embodiments, all or a portion of thedatabase 106 can be integrated with the server computing device 108 orbe located on a separate computing device or devices. For example, thedatabase 106 can comprise one or more databases, such as MySQL™available from Oracle Corp. of Redwood City, Calif.

The server computing device 108 is a combination of hardware, whichincludes a specialized machine learning processor 112 and one or morephysical memory modules, and specialized software modules 109, 110, 111,113 that execute on the machine learning processor 112 of the servercomputing device 108, to receive data from other components of thesystem 100, transmit data to other components of the system 100, andperform functions for generating annuity product recommendations andrisk strategies using a machine learning processor as described herein.

The machine learning processor 112 and the corresponding softwaremodules 109, 110, 111, and 113 are key components of the technologydescribed herein, in that these components 109-113 provide thebeneficial technical improvement of enabling the system 100 toautomatically process and analyze large sets of complex computer dataelements using a plurality of computer-generated machine learning modelsto generate user-specific actionable output relating to the selectionand optimization of annuity products. The machine learning processor 112and modules 109, 110, 111, and 113 execute artificial intelligencealgorithms to constantly improve the machine learning models byautomatically assimilating newly-collected data elements into the modelswithout relying on any manual intervention. In addition, the machinelearning processor 112 enables the rapid analysis of a large number ofmarket performance scenarios (typically on the order of tens ofthousands of scenarios) in conjunction with specifically-constructedrisk attributes, a function that both necessitates the use of aspecially-programmed microprocessor and that would not be feasible toaccomplish using general-purpose processors and/or manual techniques.

The machine learning processor 112 is a microprocessor embedded in theserver computing device 108 that is configured to retrieve data elementsfrom the database 106 and the data sources 114 for the execution of theuser needs assessment module 109, the price optimization module 110, andthe market simulation module 111. The machine learning processor 112 isprogrammed with instructions to execute artificial intelligencealgorithms that automatically process the input and traversecomputer-generated models in order to generate specialized outputcorresponding to each of the modules 109, 110, 111, and 113. The machinelearning processor 112 can transmit the specialized output to downstreamcomputing devices for analysis and execution of additional computerizedactions.

The machine learning processor 112 uses a variety of models to achievethe objectives described herein. In both the model training and modeloperation phases, the first step performed by the machine learningprocessor 112 is a data preparation step that cleans the structured andunstructured data collected. Data preparation involves eliminatingincomplete data elements or filling in missing values, constructingcalculated variables as functions of data provided, formattinginformation collected to ensure consistency, data normalization or datascaling and other pre-processing tasks.

In the training phase, initial data processing may lead to a reductionof the complexity of the data set through a process of variableselection. The process is meant to identify non-redundantcharacteristics present in the data collected that will be used in thecomputer-generated analytical models. This process also helps determinewhich variables are meaningful in analysis and which can be ignored. Itshould be appreciated that by “pruning” the dataset in this manner, thesystem achieves significant computational efficiencies in reducing theamount of data needed to be processed and thereby effecting acorresponding reduction in computing cycles required.

In addition, in some embodiments the machine learning models include aclass of models that can be summarized as supervised learning orclassification, where a training set of data is used to build apredictive model that will be used on “out of sample” or unseen data topredict the desired outcome. In one embodiment, the linear regressiontechnique is used to predict the appropriate categorization of acustomer based on input variables. In another embodiment, a decisiontree model can be used to predict the appropriate classification ofcustomer. Clustering or cluster analysis is another technique that maybe employed, which classifies data into groups based on similarity withother members of the group.

The machine learning processor 112 can also employ non-parametricmodels. These models do not assume that there is a fixed and unchangingrelationship between the inputs and outputs, but rather thecomputer-generated model automatically evolves as the data grows andmore experience and feedback is applied. Certain pattern recognitionmodels, such as the k-Nearest Neighbors algorithm, are examples of suchmodels.

Furthermore, the machine learning processor 112 develops, tests andvalidates the computer-generated models described herein iterativelyaccording to the step highlighted above. For example, the processor 112scores each model objective function and continuously selects the modelwith the best outcomes.

In some embodiments, the modules 109, 110, 111, and 113 are specializedsets of artificial intelligence-based software instructions programmedonto the dedicated machine learning processor 112 in the servercomputing device 108 and can include specifically-designated memorylocations and/or registers for executing the specialized computersoftware instructions. Further explanation of the specific processingperformed by the modules 109, 110, 111, and 113 will be provided below.

The user needs assessment module 109 collects a plurality of dataelements that specifically pertain to the user for whom the system isgenerating annuity product recommendations and related performanceinformation. For example, the user needs assessment module 109 cancollect information such as unique user identifiers (e.g., name, date ofbirth, government ID number), demographic information (e.g., age,gender, address), an investment risk tolerance for the user, investmentobjectives of the user, market index preferences, and investmentstrategy and history. As will be described in greater detail below, theuser needs assessment module 109 processes the user-related dataelements through a computer-generated user insight analysis model togenerate one or more user-specific insights—in conjunction with amulti-faceted data set as collated from a plurality of data sources114—that can be then used to generate annuity product recommendations.The user needs assessment module 109 also conducts a process to refinethe computer-generated user-specific insights based upon automatedfeedback from data sources and related analysis previously performed bythe module 109 and, in some embodiments, based upon user-providedresponses that confirm and/or diverge from the computer-generatedinsights. As a result of this feedback process, the user needsassessment module 109 generates more accurate and customized input dataelements which the module 109 then feeds back into the user insightanalysis model to re-generate the output user insight data.

The price optimization module 110 collects the user-specific insightsfrom the user needs assessment module 109 and, as will be described ingreater detail below, processes the user-specific insights through acomputer-generated annuity matching model to generate one or moreannuity product recommendations that are uniquely customized for theuser based upon: user-specific criteria; historical market data andtrends; historical product performance; and a roster of available(and/or potential) annuity products. The price optimization module 110generates the product recommendations according to a framework offeatures that are assigned customized values that the module 110predicts will satisfy the expected user needs, such as: investmentfeatures, risk management features, time commitment features, costfeatures, flexibility features, and other related features.

The market simulation module 111 collects the user-specific annuityproduct recommendations from the price optimization module 110 and, aswill be described in greater detail below, processes the user-specificannuity product recommendations through a computer-generated annuityperformance prediction model using a subset of annuity products selectedby the price optimization module and predictions of future marketperformance (including, in some embodiments, predictions based uponhypothetical market conditions). The market simulation module 111generates one or more simulated outcomes (e.g., rate of return, growthperformance, and the like) for each of the annuity products in thesubset of annuity products based upon the processing functions. In someembodiments, the market simulation module 111 generates a graphical userinterface for display to the user on the client device 102 that enablesthe user to select from a variety of potential market performancescenarios according to, e.g., the user's desired risk threshold, and themodule 111 integrates the user-selected market performance scenariosinto the outcome simulation process. The module 111 prepares a simulatedoutcome based upon each of the predictions of future market performancefor each of the annuity product recommendations selected by the priceoptimization module 110. In some embodiments, the market simulationmodule 111 generates a graphical user interface for display on theclient device 102 that presents the simulated outcomes to the user forcomparison.

The risk and hedging strategy module 113 processes information about anannuity product selected and purchased by the user through acomputer-generated risk quantification model to generate a user- andproduct-specific risk profile. Using the risk profile, the module 113then determines one or more actions that may be required of theinsurance company servicing the user's annuity product, in order for thecompany to fulfill the annuity-specific guarantees that were part of theproduct purchase. As an extension of this process, the risk and hedgingstrategy module 113 can use the generated risk profile as input to acomputer-generated hedge strategy development model to generate one ormore investment strategies for the insurance company to use as hedgesagainst the investment risk exhibited by the annuity product sold to theuser. These investment hedge strategies are advantageous because theyrepresent strategies that are specifically customized to the specificannuity products issued by the insurance company and can preciselyaccount for the individualized risk involved in servicing thoseproducts.

The data sources 114 comprise a variety of databases, data feeds, andother sources that supply data to the machine learning processor 112 tobe used in generating annuity product recommendations and riskstrategies using a machine learning processor as described herein. Thedata sources 114 can provide data to the server computing deviceaccording to any of a number of different schedules (e.g., real-time,daily, weekly, monthly, etc.) The specific data elements provided to theprocessor 112 by the data sources 114 are described in greater detailbelow.

Further to the above elements of system 100, it should be appreciatedthat the machine learning processor 112 can build and train theabove-identified computer-generated models prior to conducting theprocessing described herein. For example, the machine learning processor112 can retrieve relevant data elements from the database 106 and/or thedata sources 114 to execute algorithms necessary to build and train thecomputer-generated models (e.g., input data, target attributes) andexecute the corresponding artificial intelligence algorithms against theinput data set to find patterns in the input data that map to the targetattributes. Once the applicable computer-generated model is built andtrained, the machine learning processor 112 can automatically feed newinput data (e.g., a user-specific input data set) for which the targetattributes are unknown into the model using one or more of the modules109, 110, 111, and 113. The machine learning processor 112 then executesthe corresponding module 109, 110, 111, and 113 to generate predictionsabout how the user-specific data set maps to target attributes. Themachine learning processor 112 then creates an output set based upon thepredicted target attributes. It should be appreciated that thecomputer-generated models described herein are specialized datastructures that are traversed by the machine learning processor 112 toperform the specific functions for generating annuity productrecommendations and risk strategies as described herein. For example, inone embodiment, the models are a framework of assumptions expressed in aprobabilistic graphical format (e.g., a vector space, a matrix, and thelike) with parameters and variables of the model expressed as randomcomponents.

Finally, it should be appreciated that the techniques described hereinare applicable to new customers who desire to purchase an annuityproduct, and to current annuity policyholders that want toadvantageously view and adjust attributes of their annuity product inorder to customize their investment based upon, e.g., changing lifeconditions, changing market conditions, changing investment objectives,and the like. The machine learning processor 112 and associated modules109, 110, 111, and 113 can perform the described functions at any timeduring the annuity lifecycle and thus provide a flexible, interactivetool for annuity customer and policyholders to experience trulycustomized and unique product management.

FIG. 2 is a flow diagram of a method for generating annuity productrecommendations and risk strategies using the system 100 of FIG. 1,including the machine learning processor 112. The machine learningprocessor 112 generates (202) an input data set using the clientcomputing device 102, the database 106 and/or the data sources 114. Themachine learning processor 112 determines (202 a) a set of userinformation associated with a user (e.g., a user of client device 102),including user demographics, user risk preferences, and user objectives.For example, a user at client device 102 can access a web applicationprovided by server computing device 108 to initiate the process ofreviewing available annuity products from an insurance company andpurchasing one of the annuity products. As part of this process, theuser can log into the web application (e.g., by providing usercredentials such as a username and password) and the machine learningprocessor 112 can collect user-specific information automatically fromthe database 106 and/or data sources 114. The user information cancomprise data elements such as unique user identifiers, demographicinformation, risk tolerance information, risk and investment objectives,market index preferences, and investment strategy/history.

In one example, the machine learning processor 112 can generate andpresent one or more English-language questions to the user via agraphical user interface that request information. Beneficially, themachine learning processor 112 provides a series of questions that donot require specialized knowledge about investment, risk, marketperformance and the like to the user in order to develop and optimize auser profile to be used in the processes described herein. For example,the machine learning processor 112 determines risk tolerance informationfor the user that relates to the degree of risk that the user is willingto undertake and/or the degree of protection from risk that the userdesires. To do this, the machine learning processor 112 generates agraphical user interface that allows prospective customers and currentpolicyholders to select, in plain English, the degree of risk toleranceor risk protection that they desire. This feature provides a majorimprovement over the difficult to understand and inflexible riskparameters that current art computerized annuity systems offer. Themachine learning processor 112 guides the user through a series of plainEnglish questions that will allow the user to articulate his or her riskprofile/preferences. Then, the machine learning processor 112 can offerand show different configurations of return possibilities and riskmanagement features, with clearly disclosed pricing differences. Forexample, a question that the machine learning processor 112 can presentto the user may be a series of statements from which the user choosesone that most fits his or her risk preferences, such as: “I am willingto put 3 years of annuity earning at risk in order to benefit from moreupside participation in the S&P 500 index.” The machine learningprocessor 112 associates the selected statement with a correspondingrisk factor and inserts the risk factor into the user's risk profile aspart of the user information.

Also, as part of the input data set generation, the machine learningprocessor 112 determines (202 b) a set of market index information,including historical market index information, current market indexinformation, and forecast market index information. For example, themarket index information can include specific market index values,trends and changes in market indices over time, correlation of marketindex information to other economic conditions or factors (e.g.,unemployment, GNP, and so forth). As will be described below, themachine learning processor 112 can use the collected market indexinformation as part of the user needs assessment, annuityrecommendation, risk quantification, and hedge strategy processes.

In addition, the machine learning processor 112 determines (202 c) oneor more available annuity products based upon the set of userinformation collected. In some embodiments, the machine learningprocessor 112 evaluates the user information against characteristicsassociated with a predefined catalog of annuity products (as provided byan insurance company data source) to determine whether one or more ofthe annuity products is applicable or available to the user. In someembodiments, the machine learning processor 112 generates a hybridannuity product that may not directly correspond with an availableannuity product but has certain features and characteristics optimizedbased upon the user information (e.g., a possible annuity productconfiguration). The machine learning processor 112 can use the set ofavailable annuity products as part of the user needs assessment andannuity recommendation processes described herein.

Once the machine learning processor 112 has generated the input dataset, the processor 112 executes (204) the user needs assessment module109 to traverse a computer-generated user insight analysis model withthe user information as input in order to generate one or moreuser-specific insights that relate to expected and/or desired attributesthat a user would seek in an annuity product (e.g., with respect to risktolerance, investment objectives, rate of return, market indexpreferences, and the like).

FIG. 3 is a workflow diagram of a method for generating user-specificinsight data using the system 100 of FIG. 1, specifically using themachine learning processor 112 to execute the user needs assessmentmodule 109. The machine learning processor 112 initializes the userneeds assessment module 109 with user-specific input data 302 (e.g.,from the input data set). The machine learning processor 112 alsocollects specific input data elements from a variety of data sources114, including historical user needs and insights (i.e., past outputsfor the user as generated by the user needs assessment module 109),historical client behavior, historical annuity product performance,market performance (e.g., historical, current, projected), predefinedrules, and one or more available annuity product configuration(s). Themachine learning processor 112 executes the artificial intelligencealgorithms of the user needs assessment module 109 against thecomputer-generated user insight analysis model to generate user-specificinsight data 304 (e.g., target attributes and predicted values for userneeds based upon traversal of the model).

At this stage, in some embodiments, the machine learning processor 112captures the user-specific insight data as output for the priceoptimization module 110 (as described below). In some embodiments, themachine learning processor 112 conducts an insight acceptance/needsrefinement process to adjust the output set and provide more accurateuser insight data. For example, the machine learning processor 112executes a user insights acceptance process 306 to confirm the accuracyof the user insight data generated by the module 109. In one embodiment,the machine learning processor 112 presents the user insight data on aGUI associated with the client device 102, and the user can validate orreject the proposed insight data via user input. Next, the machinelearning processor 112 receives the user feedback on the insight dataand executes a user needs refinement process 308 to automatically adjustthe existing user-specific input data (e.g., user objectives, investmentstrategies, risk tolerances, and so forth) and to generate additionaluser-specific input data to be used as an input data set forre-processing by the user needs assessment module 109. In this way, themachine learning processor 112 can continuously execute the user needsassessment process depicted in FIG. 3 in order to generate more accurateuser-specific insights and to refine the computer-generated user insightanalysis model for subsequent training and processing.

Turning back to FIG. 2, once the machine learning processor 112 hasgenerated a set of accepted user-specific insight data, the machinelearning processor executes (206) the price optimization engine 110 totraverse a computer-generated annuity matching model to select a subsetof the available annuity products that are associated with productcharacteristics that match one or more data elements that match theuser-specific insight data and to generate one or more annuity productrecommendations for the user that include a framework of specificmatched features.

FIG. 4 is a workflow diagram of a method for generating user-specificproduct recommendations using the system 100 of FIG. 1, specificallyusing the machine learning processor 112 to execute the priceoptimization module 110. The machine learning processor 112 initializesthe price optimization module 109 with the user-specific insight data402 (e.g., from the user needs assessment module 109 output). Themachine learning processor 112 also collects specific input dataelements from a variety of data sources 114, including historical userneeds and insights (i.e., past outputs for the user as generated by theuser needs assessment module 109), historical client behavior,historical annuity product performance, market performance (e.g.,historical, current, projected), predefined rules, and one or moreavailable annuity product configuration(s). The machine learningprocessor 112 executes the artificial intelligence algorithms of theprice optimization module 110 against the computer-generated annuitymatching model to generate user-specific annuity product recommendationdata 404 (e.g., target attributes and predicted values for annuityproduct recommendations specifically tailored to the user's needs basedupon traversal of the model).

As part of the price optimization module 110 execution, the machinelearning processor 112 integrates a lifecycle risk management process406 into the annuity product recommendation determination. The lifecyclerisk management process 406 comprises a series of algorithms andinstructions that enable adjustment of risk management choicesassociated with the annuity products to beneficially provide a flexibleand dynamic annuity modeling process. The lifecycle risk managementprocess 406 provides practical and transparent risk management choicesto the prospective annuity customer and the policyholder that can bechanged as risk preferences vary by stage of life. As the need forinvestment certainty increases the risk control parameters in the policycan be changed.

For example, the policyholder may choose to increase the amount ofguaranteed lifetime income to the annuity. Or the policyholder maychoose to incorporate a minimum guaranteed accumulation value. Thelifecycle risk management process 406 can provide the ability to selecta desired risk management option. Typically, in the field of variableproducts, risk protection features such as principal protection are oflimited availability. Also, in general, the range of options are alsolimited in annuity products. This is at least because the selection ismade as part of executing the annuity contract and is locked into theannuity while in-force. In addition, advanced risk protection techniquesare only available at a product level such as by participating in a fundas opposed to an individual level (e.g., a user-selected risk managementfor that user's annuity).

Preferably, the machine learning processor 112 leverages the lifecyclerisk management process 406 to provide the ability for a user to selecta desired risk management option (from among different options)—duringthe annuity purchase process and at a point after the annuity isin-force. The machine learning processor 112 can provide this selectionoption at any time (e.g., on-demand) or can make the selection optionavailable based on some other temporal criteria, such as daily ormonthly.

The machine learning processor 112 can generate or support a GUI thatprovides interactive screens (e.g., via mobile or browser basedinterface) that permit the user to make these selections before andafter the annuity is in-force. The flexibility and user-managed featuresof the annuity can be very attractive to consumers. The system hasunderlying backend components that preferably determine availablerisk-selection options and presents those options to a user on anindividualized basis.

As an additional feature of the lifecycle risk management process 406,the machine learning processor 112 can determine and display the cost ofa risk management selection to the current user. For example, themachine learning processor 112 can generate a graphical user interfacethat displays risk management options and their associated costs (e.g.,that the selection of risk management option #1 will involve a certaincost such as 0.2% annually of principal and earnings in the annuity).Other measures of cost may also be used such as a dollar amount. Thisway, the machine learning processor 112 clearly communicates the impactof a risk protection selection to the user. Also, depending on the riskmanagement option, the cost can be customized or individualized to thatannuity.

The user-specific product recommendations generated by the machinelearning processor 112 via the price optimization module 110 areassociated with a number of different features that define anddifferentiate the specific annuity products. For example, theuser-specific product recommendations can include features such as:investment features (e.g., which indices the annuity product provides orallows for investment, the upside of the product); risk managementfeatures (e.g., caps, floors, guarantees, curves); time commitmentfeatures (e.g., policy durations, minimum investment or purchaselength); cost features; flexibility features; and other such features.

Turning back to FIG. 2, once the machine learning processor 112 hasdetermined the set of annuity product recommendations as set forthabove, the machine learning processor 112 executes (208) the marketsimulation module 111 to traverse a computer-generated annuityperformance prediction model using: the subset of annuity productsselected by the price optimization module 110; and one or morepredictions of market performance.

FIG. 5 is a workflow diagram of a method for generating user-specificand product-specific simulated outcomes for annuity productrecommendations using the system 100 of FIG. 1, specifically using themachine learning processor 112 to execute the market simulation module111. The machine learning processor 112 initializes the marketsimulation module 111 with the user-specific product recommendations 502(e.g., from the price optimization module 110 output). In someembodiments, the machine learning processor 112 requests one or moremarket performance prediction values (e.g., prediction 504) from theclient computing device 102. For example, the machine learning processor112 can present one or more future market performance scenarios (e.g.,weak performance, normal performance, strong performance) to a user atclient device 102. In one embodiment, the one or more future marketperformance scenarios for selection by the user are displayed as marketindex graphs that depict the market performance over time. The machinelearning processor 112 receives the market performance prediction valuesfrom the client computing device 102 for integration with the input forthe market simulation module 111.

The machine learning processor 112 also collects specific input dataelements from a variety of data sources 114, including historicalannuity product performance, market performance (e.g., historical,current, projected), and predefined rules. The machine learningprocessor 112 executes the artificial intelligence algorithms of themarket simulation module 111 against the computer-generated annuityperformance prediction model to generate one or more simulated outcomes506 for each of the annuity products selected by the price optimizationmodule 110 (e.g., target attributes and predicted values for annuityproduct simulated future outcomes based upon traversal of the model). Asshown in FIG. 5, the simulated outcomes 506 are individualized forspecific annuity products selected by the price optimization engine 110and reflect simulated outcomes for the annuity products based uponcertain future market performance predictions/assumptions.

In one embodiment, the machine learning processor 112 generates (210) agraphical user interface to display the annuity product recommendationsfrom the price optimization engine 110, and display the simulatedoutcomes 506 on the client device 102 (e.g., as line graphs depictingthe simulated performance of the respective annuity products). In thisembodiment, the graphical user interface can display the graphs in aform suitable for evaluation and comparison by a user of the clientdevice 102. It should be appreciated that the graphical user interfacecan display the simulated outcome performance of the annuity inconjunction with the selected index and risk management options. This isa valuable tool because the cumulative effect of the risk managementoption and its related cost may not be evident to a user without complexcalculations that are not typically available to consumers. For example,information about a 1% cost for a risk protection feature that isiteratively applied every year and its relationship to gains or lossesof an index may be easy to understand at a very basic level for a singleyear but the long term impact is difficult to project for individuals.The graphical user interface generated by the machine learning processor112 in this step, therefore, provides a valuable self-enabling tool thatpermits the user to make informed decisions about strategy for theannuity.

Exemplary Use Case

The following section is an exemplary use case of the process ofgenerating annuity product recommendations using the system 100 ofFIG. 1. A user at client computing device 102 establishes a connectionwith a web application executing on server computing device 108 (e.g.,via browser). The user logs into the web application (e.g., usingpreviously-created user credentials) or creates a user profile if theuser is new to the application. FIG. 6 depicts an exemplary graphicaluser interface 600 generated by the server computing device 108 anddisplayed on the client device 102 for the purposes of creating a useraccount and user profile. As shown in FIG. 6, the graphical userinterface 600 includes input fields 602 for the user to provide dataelements such as name, SSN, address, email and phone number. Once theuser clicks the submit button 604, the server computing device 108creates a user account and baseline user profile.

The web application, in conjunction with the machine learning processor112, then launches a needs assessment questionnaire and displays acorresponding user interface on the client device 102. The needsassessment questionnaire includes a series of data input elements(including, in some cases, optional data input elements) relating totopics such as: user behavioral information (e.g., risk tolerance,savings vs. spending prioritization), demographic data (e.g., age,gender, marital status, # of dependents, employment, and the like),investment objectives (e.g., statements with a set of responses(“Strongly Agree,” “Strongly Disagree,” etc.) for the user to rank hisor her agreement or disagreement, in order to understand the user'spreferences and objectives). The questionnaire may also include freeformtext fields for the user to input customized information. FIG. 7 depictsan exemplary graphical user interface 700 generated by the servercomputing device 108 and displayed on the client device 102 for thepurposes of collecting user needs assessment input. As shown in FIG. 7,the graphical user interface 700 includes data input elements such assliders 702 and drop-down input boxes 704 that enable the user toprovide input such as risk preferences, life stage, index selections andweights, product size, term, and linked accounts.

Following the needs assessment questionnaire, the user grants the webapplication access to the user's asset-related information, such asfinancial accounts (e.g., account types, account balances, transactionhistory), real property, personal property, and so forth—as well as theuser's liabilities (e.g., revolving debt, mortgage debt, alimonypayments, etc.). In one example, this asset-related information isobtained from the data sources 114. As part of the data collectionprocess, the machine learning processor 112 can reconfigure or reformatthe asset-related information so that the information can be efficientlyassimilated into the machine learning models as described above. Forexample, many years of the user's historical financial and asset-relateddata can be summarized into a set of variables such as: account type,average balance(s) over time, number of transactions per period, typesof transactions (e.g., stock trades vs. deposits vs. consumer spending,and so forth). As a point of comparison, in traditional annuity systems,this type of asset-related data is typically collected manually (e.g.,by a financial advisor) but advantageously the system 100 describedherein can access and collect the asset-related data automatically,perform complex data mining and modeling on the information (e.g., usingthe machine learning processor 112) in order to discern meaningfulpatterns in the data that are used in profiling).

Next, the machine learning processor 112 validates the responses to thequestionnaire provided by the user in association with the asset-relateddata that was obtained automatically. For example, the machine learningprocessor 112 can identify inconsistencies between the two sets of data(e.g., the user's self-reported spending habits do not align with thetransaction histories for that user's financial accounts) and ask theuser at client device 102 for clarification and/or additional data.

Once the machine learning processor 112 has completed validating theuser's data set (e.g., questionnaire responses and asset-related data),the machine learning processor 112 executes the modules 109, 110, 111described above to “categorize” the user in the computer-generatedmodels across many dimensions. For example, each computer-generatedmodel is trained using a coarse and simplistic segmentation of customertypes based on expert data (e.g., from financial advisors), analysis ofexisting customers, industry standards (as obtained from data sources114), and/or customer self-identification. It should be appreciated thateach segment has certain distinguishing features and the machinelearning processor 112 can traverse the computer-generated models inorder to assign probabilistically a new customer into a specific segment(i.e., into which segment is the customer most likely to fit?).

The machine learning processor 112 then generates an initial userprofile based upon the categorization described above. Using the userneeds assessment module 109, the machine learning processor 112generates a graphical user interface containing one or more userinsights that are consistent with a user having the same or similar userprofile. FIG. 8 depicts an exemplary graphical user interface 800generated by the server computing device 108 and displayed on the clientdevice 102 for the purposes of presenting user insights and collectinguser feedback on the insights. As shown in FIG. 8, the insights 802 area series of statements on an agree/disagree spectrum and the user atclient device 102 can select the option along the spectrum that bestmatches his or her agreement with each statement. In other embodiments,the insights can include true/false questions, and in some cases, theGUI includes an accept/reject option for the entire set of insights. Asshown in FIG.> 8, some exemplary user insights include, but are notlimited to:

-   -   “I value the possibility of greater upside potential over risk        of losses;”    -   “I am comfortable committing to a five year term;”    -   “I do not need to use any of the funds in my account for at        least ten years;”    -   “I would like to have additional exposure to international stock        market indices in my financial products;”

If the user rejects the insights (or submits answers that areinconsistent with expectations that the machine learning processor 112has generated based upon the model traversal), the machine learningprocessor can conduct the user needs refinement process 308 as describedwith respect to FIG. 3 above. If the user accepts the user insights, themachine learning processor 112 creates a user profile and incorporatesthe insights into the profile. It should be appreciated that thefeedback loop described in FIG. 3 (i.e., continuously collecting dataand returning user insights) provides the technical advantage ofincremental improvement to the predictive machine learning process andthe computer-generated model of profiling/categorizing users and theirneeds. Over time, the machine learning processor 112 creates moregranular segments of user categories/profiles into the model, for aneven more multi-faceted and dimensional model.

Next, the machine learning processor 112 feeds the user profile from theuser needs assessment module 109 as input into execution of the priceoptimization module 110, as described previously. The outcome of thisstep of the process is generating a bespoke annuity product with acustomized set of features that is both feasible for the insurancecompany to issue (e.g., in terms of product offering, risk level, andthe like) and which is most likely to meet the user's needs as expressedin the user profile. The machine learning processor 112 trains thecomputer-generated annuity matching model based upon observed productchoices of existing customers and, in some embodiments, based uponadvice of experts and associated business rules. The machine learningprocessor 112 further analyzes market conditions (e.g., market indexlevels, volatility, interest rates, etc.), the insurance company'sreal-time risk exposure and the expected product profitability todynamically price the possible features of the annuity, accounting for:i) the non-linear interactions caused by combinations of features, ii)the risk tolerance of the insurance company, and iii) the company'sprofit targets. It should be appreciated that execution of the priceoptimization module 110 and traversal of the computer-generated annuitymatching model is focused on finding an annuity product that mostclosely addresses the needs captured in the user profile, which also haslow costs and is feasible and profitable for the insurance company toissue.

The machine learning processor 112 generates a graphical user interfacecontaining one or more annuity product configurations as output fromexecution of the price optimization engine 110. FIG. 9 depicts anexemplary graphical user interface 900 generated by the server computingdevice 108 and displayed on the client device 102 for the purposes ofdisplaying annuity product recommendations. As shown in FIG. 9, thegraphical user interface 900 depicts a plurality of annuity productconfigurations 902, 904. Each configuration 902, 904 is comprised of agranular description of the product feature set 902 a, 904 arespectively, such as: tenor, index(es), upside participation formula,risk management features, guaranteed return, income benefits, fees, andthe like. The user can then select from several different actions withrespect to the product configurations 902, 904 such as: modeling theconfiguration, editing the configuration, or buying theconfiguration—each action is associated with a corresponding button(e.g., button sets 902 a, 902 b respectively). The user can also providefeedback about the presented annuity product configuration(s). Forexample, the user can indicate that the features for one or more of theproduct configurations does not meet his or her needs (e.g., byproviding a rating 902 c, 904 c for each configuration). The machinelearning processor 112 can receive the feedback from the client device102 and use the feedback as further input to the price optimizationmodule 110 for re-execution to determine other product configuration(s)that may more closely match the user's needs. It should be appreciatedthat the machine learning processor 112 can execute the priceoptimization module 110 and traverse the computer-generated annuitymatching model many times until a product configuration is generatedthat is accepted by the user.

It should also be appreciated that the machine learning processor 112can calibrate the computer-generated annuity matching model using thefeedback obtained during this process. The types of feedback caninclude, but are not limited to: direct feedback (e.g., as obtained fromthe user via client device 102), customer abandonment (i.e., user failedto select a product configuration after being presented with severaloptions, which could mean that there was an imperfect match between theuser needs and proposed configurations). Feedback can also be obtainedfrom post-sale annuity changes—e.g., by giving the user the option tochange annuity product features at many points during the life of theproduct, the system can determine whether the changes result from animperfect initial match of the annuity product to user needs and/or achange in user needs over time.

Once the user has selected one or more of the potential productconfigurations, the machine learning processor 112 executes the marketsimulation module 111 and traverses the computer-generated annuityperformance prediction model to generate the simulated outcomes for eachproduct configuration. For example, the machine learning processor 112processes the product configuration(s) through a series of stochasticmarket simulations (e.g., using different expected or predicted marketconditions) to generate a detailed graphical view of possible evolutionsof, e.g., account value in different future paths of market returns.

FIG. 10 depicts an exemplary graphical user interface 1000 generated bythe server computing device 108 and displayed on the client device 102for the purposes of displaying a simulated outcome for a particularannuity product recommendation. As shown in FIG. 10, the graphical userinterface 1000 includes a series of sliders 1002 that enables the userto modify certain potential market conditions and see how those modifiedconditions affect the expected performance of the annuity product. Asshown, the expected performance is displayed as one or moregraphs/charts 1004 that show various performance results and indicatorsfor the product.

Risk Quantification and Hedging Strategies

Another advantageous feature of the system 100 described herein connectsan insurance company's investment and risk management systems to anartificial intelligence modeling process executed by the machinelearning processor 112 in order to provide risk quantification andcorresponding hedging strategy options to the company (e.g., post-saleof the annuity products to customers). To implement this connection, themachine learning processor 112 receives information specifying the riskmanagement selection(s) of users and in response, processes theselection(s) and determines in relation to existing positions whether tomodify the company's investment and risk management positions. Themachine learning processor 112 executes the risk and hedging strategymodule 113 that processes and determines the cost of risk managementoptions for individual users or policyholders, then proceeds to developdistinct investment hedging strategies based upon the risk managementcost in order to ensure that the insurance company can fulfill theproduct guarantees to its customers.

FIG. 11 is a workflow diagram of a method for generating a user-specificand product-specific risk profile using the system 100 of FIG. 1,specifically using the machine learning processor 112 to execute therisk and hedging strategy module 113. The machine learning processor 112initializes the risk and hedging strategy module 113 with auser-specific annuity product selection (e.g., as purchased by the user)and with insurance company specifications 1102 relating to the annuityproduct. The machine learning processor 112 also collects specific inputdata elements from a variety of data sources 114, including historicaluser needs and insights (i.e., past outputs for the user as generated bythe user needs assessment module 109), historical client behavior,historical annuity product performance, market performance (e.g.,historical, current, projected), predefined rules, and one or moreavailable annuity product configuration(s). The machine learningprocessor 112 executes the artificial intelligence algorithms of therisk and hedging strategy module 113 against a computer-generated riskquantification model to generate user-specific and product-specific riskprofile data 1104 (e.g., target attributes and predicted values foranticipated investment and market risk specifically associated with theannuity product purchased by the user based upon traversal of themodel).

At this stage, in some embodiments, the machine learning processor 112captures the user-specific and product-specific risk profile data 1104as output to determine an appropriate investment hedging strategy aswill be explained below. In some embodiments, the machine learningprocessor 112 conducts a risk profile review and product/specificationrefinement process to adjust the risk profile data output and providemore accurate risk profile data. For example, the machine learningprocessor 112 executes a risk profile review process 1106 to validatethe accuracy of the risk profile data generated by the module 113. Inone embodiment, the machine learning processor 112 presents the riskprofile data 1104 on a GUI associated with the client device 102, andthe user at the client device 102 can validate or reject the riskprofile data via user input. Next, the machine learning processor 112receives the user feedback on the risk profile data and executes anannuity product/specification refinement process 1108 to automaticallyadjust the existing risk profile data (e.g., risk of loss in value ofthe investments associated with the annuity, investment strategies, risktolerances, and so forth) and to generate additional user-specificproduct selection and insurance company specifications data to be usedas an input data set for re-processing by the risk and hedging strategymodule 113. In this way, the machine learning processor 112 cancontinuously execute the risk profile review and product/specificationrefinement process depicted in FIG. 11 in order to generate moreaccurate risk profile data and to refine the computer-generated riskquantification model for subsequent training and processing.

For example, the machine learning processor 112 can run granular riskanalytics in real-time on the annuity product sold by the company to auser, or products sold to multiple users. Exemplary risk analyticsinclude, but are not limited to: exposure to mortality, morbidity,surrenders, equity market, interest rates, and volatility. The riskanalytics process also summarizes the implications of annuity productguarantees, rates of return, and risk management features of theannuities that have been sold by the insurance company—which are thenincorporated into the user-specific and product-specific risk profile.It should be appreciated that the machine learning processor 112 candynamically update the aggregated risk analytics using the risk andhedging strategy module 113 to incorporate newly-sold products, as wellas changes in market conditions or actuarial expectations.

Once the machine learning processor 112 has developed a user-specificand product-specific risk profile 1104 using the risk and hedgingstrategy module 113, the machine learning module 112 can leverage therisk profile to generate one or more hedging strategies for theinsurance company to mitigate the potential risk associated with theannuity products it has sold and to ensure that any guarantees for thoseproducts are met.

FIG. 12 is a workflow diagram of a method for generating hedgestrategies using the system 100 of FIG. 1, specifically using themachine learning processor 112 executing the risk and strategy hedgingmodule 113. The machine learning processor 112 initializes the risk andhedging strategy module 113 with investment risk objectives data 1202(e.g., as determined by the insurance company). The machine learningprocessor 112 also collects specific input data elements from a varietyof data sources 114, including historical user needs and insights (i.e.,past outputs for the user as generated by the user needs assessmentmodule 109), historical client behavior, historical annuity productperformance, market performance (e.g., historical, current, projected),predefined rules, and one or more available annuity productconfiguration(s). The machine learning processor 112 executes theartificial intelligence algorithms of the risk and hedging strategymodule 113 against a computer-generated hedging strategy developmentmodel to generate hedge strategy data 1204 (e.g., target attributes andpredicted values for potential investment hedges specifically associatedwith the annuity products purchased by users based upon traversal of themodel).

In this step, the machine learning processor 112 takes insurance companyobjectives as input into the computer-generated hedging strategydevelopment model and traverses the model with calculated analytics topropose a portfolio of financial instruments for the insurance companyto use in hedging the annuity product risk as set forth in the riskprofile. The proposed portfolio is based upon a deep database (e.g.,database 106 of historical performance of a large set of financialinstruments, which advantageously provides the technical advantage ofbacktesting the proposed portfolio. The database 106 also containsdetailed information about the universe of usable financial instrumentsthat the insurance company can leverage for hedging.

At this stage, in some embodiments, the machine learning processor 112captures the hedge strategy data 1204 as output for transmission toother computing systems of the insurance company to execute the hedgingstrategy (e.g., re-align assets, execute transactions, and the like).For example, the machine learning processor 112 connects tobroker-dealer computer systems to provide real-time bids and/or offerson specific hedging instruments, which enables the insurance company todynamically implement its hedging program.

In some embodiments, the machine learning processor 112 conducts a hedgestrategy review and risk objective refinement process to adjust thehedge strategy data output and provide more accurate hedge strategydata. For example, the machine learning processor 112 executes a hedgestrategy review process 1206 to validate the accuracy of the hedgestrategy data generated by the module 113. In one embodiment, themachine learning processor 112 presents the hedge strategy data 1204 ona GUI associated with the client device 102, and the user at the clientdevice 102 can validate or reject the hedge strategy data via userinput. Next, the machine learning processor 112 receives the userfeedback on the risk profile data and executes a risk objectiverefinement process 1208 to automatically adjust the existing hedgestrategy data and to generate additional and risk objectives data to beused as an input data set for re-processing by the risk and hedgingstrategy module 113. In this way, the machine learning processor 112 cancontinuously execute the hedging strategy review and risk objectiverefinement process depicted in FIG. 12 in order to generate moreaccurate hedging strategy data and to refine the computer-generatedhedging strategy development model for subsequent training andprocessing.

It should be appreciated that the feedback loop described in FIG. 12(i.e., continuously collecting risk and investment data and returninghedging strategies) provides the technical advantage of incrementalimprovement over time to the predictive machine learning process and thecomputer-generated model of generating hedging strategies (i.e., did thehedges achieve the insurance company's objectives as expected?). Overtime, the machine learning processor 112 integrates more granularsegments of hedging strategies into the model, for an even moremulti-faceted and dimensional model.

Method steps can be performed by one or more special-purpose processorsexecuting a computer program to perform functions of the invention byoperating on input data and/or generating output data. Method steps canalso be performed by, and an apparatus can be implemented as,special-purpose logic circuitry, e.g., a FPGA (field programmable gatearray), a FPAA (field-programmable analog array), a CPLD (complexprogrammable logic device), a PSoC (Programmable System-on-Chip), ASIP(application-specific instruction-set processor), or an ASIC(application-specific integrated circuit), or the like. Subroutines canrefer to portions of the stored computer program and/or the processor,and/or the special circuitry that implement one or more functions.

Processors suitable for the execution of a computer program include, byway of example, special-purpose microprocessors. Generally, a processorreceives instructions and data from a read-only memory or a randomaccess memory or both. The essential elements of a computer are aspecialized processor for executing instructions and one or morespecifically-allocated memory devices for storing instructions and/ordata. Memory devices, such as a cache, can be used to temporarily storedata. Memory devices can also be used for long-term data storage.Generally, a computer also includes, or is operatively coupled toreceive data from or transfer data to, or both, one or more mass storagedevices for storing data, e.g., magnetic, magneto-optical disks, oroptical disks. A computer can also be operatively coupled to acommunications network in order to receive instructions and/or data fromthe network and/or to transfer instructions and/or data to the network.Computer-readable storage mediums suitable for embodying computerprogram instructions and data include all forms of volatile andnon-volatile memory, including by way of example semiconductor memorydevices, e.g., DRAM, SRAM, EPROM, EEPROM, and flash memory devices;magnetic disks, e.g., internal hard disks or removable disks;magneto-optical disks; and optical disks, e.g., CD, DVD, HD-DVD, andBlu-ray disks. The processor and the memory can be supplemented byand/or incorporated in special purpose logic circuitry.

To provide for interaction with a user, the above described techniquescan be implemented on a computing device in communication with a displaydevice, e.g., a CRT (cathode ray tube), plasma, or LCD (liquid crystaldisplay) monitor, a mobile device display or screen, a holographicdevice and/or projector, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse, a trackball, a touchpad,or a motion sensor, by which the user can provide input to the computer(e.g., interact with a user interface element). Other kinds of devicescan be used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, and/ortactile input.

The above-described techniques can be implemented in a distributedcomputing system that includes a back-end component. The back-endcomponent can, for example, be a data server, a middleware component,and/or an application server. The above described techniques can beimplemented in a distributed computing system that includes a front-endcomponent. The front-end component can, for example, be a clientcomputer having a graphical user interface, a Web browser through whicha user can interact with an example implementation, and/or othergraphical user interfaces for a transmitting device. The above describedtechniques can be implemented in a distributed computing system thatincludes any combination of such back-end, middleware, or front-endcomponents.

The components of the computing system can be interconnected bytransmission medium, which can include any form or medium of digital oranalog data communication (e.g., a communication network). Transmissionmedium can include one or more packet-based networks and/or one or morecircuit-based networks in any configuration. Packet-based networks caninclude, for example, the Internet, a carrier internet protocol (IP)network (e.g., local area network (LAN), wide area network (WAN), campusarea network (CAN), metropolitan area network (MAN), home area network(HAN)), a private IP network, an IP private branch exchange (IPBX), awireless network (e.g., radio access network (RAN), Bluetooth, nearfield communications (NFC) network, Wi-Fi, WiMAX, general packet radioservice (GPRS) network, HiperLAN), and/or other packet-based networks.Circuit-based networks can include, for example, the public switchedtelephone network (PSTN), a legacy private branch exchange (PBX), awireless network (e.g., RAN, code-division multiple access (CDMA)network, time division multiple access (TDMA) network, global system formobile communications (GSM) network), and/or other circuit-basednetworks.

Information transfer over transmission medium can be based on one ormore communication protocols. Communication protocols can include, forexample, Ethernet protocol, Internet Protocol (IP), Voice over IP(VOIP), a Peer-to-Peer (P2P) protocol, Hypertext Transfer Protocol(HTTP), Session Initiation Protocol (SIP), H.323, Media Gateway ControlProtocol (MGCP), Signaling System #7 (SS7), a Global System for MobileCommunications (GSM) protocol, a Push-to-Talk (PTT) protocol, a PTT overCellular (POC) protocol, Universal Mobile Telecommunications System(UMTS), 3GPP Long Term Evolution (LTE) and/or other communicationprotocols.

Devices of the computing system can include, for example, a computer, acomputer with a browser device, a telephone, an IP phone, a mobiledevice (e.g., cellular phone, personal digital assistant (PDA) device,smart phone, tablet, laptop computer, electronic mail device), and/orother communication devices. The browser device includes, for example, acomputer (e.g., desktop computer and/or laptop computer) with a WorldWide Web browser (e.g., Chrome™ from Google, Inc., Microsoft® InternetExplorer® available from Microsoft Corporation, and/or Mozilla® Firefoxavailable from Mozilla Corporation). Mobile computing device include,for example, a Blackberry® from Research in Motion, an iPhone® fromApple Corporation, and/or an Android™-based device. IP phones include,for example, a Cisco® Unified IP Phone 7985G and/or a Cisco® UnifiedWireless Phone 7920 available from Cisco Systems, Inc.

Comprise, include, and/or plural forms of each are open ended andinclude the listed parts and can include additional parts that are notlisted. And/or is open ended and includes one or more of the listedparts and combinations of the listed parts.

One skilled in the art will realize the subject matter may be embodiedin other specific forms without departing from the spirit or essentialcharacteristics thereof. The foregoing embodiments are therefore to beconsidered in all respects illustrative rather than limiting of thesubject matter described herein.

What is claimed is:
 1. A system comprising: a server computing devicecommunicably coupled to a database computing device and having a machinelearning processor programmed with an instruction to execute anartificial intelligence algorithm, the server computing deviceprogrammed to: generate an input data set by: determining a set of userinformation associated with a user, including user demographics, userrisk preferences, and user objectives; determining a set of market indexinformation, including historical market index information, currentmarket index information, and forecast market index information; anddetermining one or more available annuity products using the set of userinformation; execute, by the machine learning processor, a priceoptimization module to traverse a computer-generated annuity matchingmodel using the input data set to select a subset of the availableannuity products that are associated with product characteristics thatmatch one or more of the user objectives and generate one or moreannuity product recommendations for the user based upon the subset ofannuity products; and execute, by the machine learning processor, amarket simulation module to traverse a computer-generated annuityperformance prediction model using the subset of annuity productsselected by the price optimization module and one or more predictions ofmarket performance, the market simulation module generating one or moresimulated outcomes for each of the annuity products in the subset ofannuity products; the system further comprising a client computingdevice communicably coupled to the server computing device, the clientcomputing device configured to: generate a graphical user interface fordisplay to the user via a display device, the graphical user interfaceincluding one or more visual representations of each of: the one or moreannuity product recommendations and the one or more simulated outcomes,wherein the machine learning processor and the modules execute theartificial intelligence algorithm to improve the computer-generatedmodels by automatically assimilating newly-collected input data elementsfrom an external data source into the computer-generated models withoutrelying on manual intervention, and wherein the server computing devicefurther configured to build, by the machine learning processor, thecomputer-generated annuity matching model by training a machine learningalgorithm programmed on the machine learning processor against atraining data set.
 2. The system of claim 1, wherein the input data setis generated by retrieving at least a portion of the user information,market index information, and available annuity products from theexternal data source.
 3. The system of claim 1, the server computingdevice further configured to receive at least a portion of the userinformation from the user.
 4. The system of claim 3, the servercomputing device further configured to execute, by the machine learningprocessor, a user needs assessment module to traverse acomputer-generated user insight analysis model using the userinformation, the user needs assessment module generating one or moreexpected user attributes.
 5. The system of claim 4, the server computingdevice further configured to transmit the one or more expected userattributes to the client computing device for display to the user. 6.The system of claim 5, the server computing device further configured toreceive a response to the one or more expected user attributes from theclient computing device and re-execute the user needs assessment moduleusing the received response to adjust one or more of the expected userattributes.
 7. The system of claim 1, wherein the graphical userinterface includes one or more input controls to enable interaction withand manipulation of the one or more visual representations by the userof the client computing device.
 8. The system of claim 1, the servercomputing device further configured to build, by the machine learningprocessor, the computer-generated annuity prediction performance modelby training a machine learning algorithm programmed on the machinelearning processor using the input data set.
 9. The system of claim 1,wherein the product characteristics are reference market indices,participation rate, downside protection, commitment term, incomebenefits, and fees.
 10. The system of claim 1, wherein the user riskpreferences include a risk tolerance.
 11. The system of claim 1, whereinthe machine learning processor returns the generated one or more annuityproduct recommendations to the price optimization module as input toupdate the computer-generated annuity matching model.
 12. The system ofclaim 1, wherein the machine learning processor returns the generatedone or more simulated outcomes to the market simulation module as inputto update the computer-generated annuity performance prediction model.13. The system of claim 1, wherein the one or more predictions of marketperformance include at least one prediction of market performancereceived from the client computing device as input by the user.
 14. Thesystem of claim 1, wherein the one or more predictions of marketperformance correspond to a performance of one or more market indices.15. The system of claim 1, wherein the one or more simulated outcomesfor each of the annuity products correspond to an expected rate ofreturn for the annuity product.
 16. The system of claim 1, wherein theserver computing device is further configured to analyze marketconditions, determine risk exposure from issued annuities, and expectedprofitability to dynamically price annuity products.
 17. A methodcomprising: generating, by a server computing device communicablycoupled to a database and having a machine learning processor programmedwith an instruction to execute an artificial intelligence algorithm, aninput data set by: determining a set of user information associated witha user, including user demographics, user risk preferences, and userobjectives; determining a set of market index information, includinghistorical market index information, current market index information,and forecast market index information; and determining one or moreavailable annuity products using the set of user information; executing,by the machine learning processor, a price optimization engine totraverse a computer-generated annuity matching model using the inputdata set to select a subset of the available annuity products that areassociated with product characteristics that match one or more of theuser objectives and generate one or more annuity product recommendationsfor the user based upon the subset of annuity products; and executing,by the machine learning processor, a market simulation engine totraverse a computer-generated annuity performance prediction model usingthe subset of annuity products selected by the price optimization engineand one or more predictions of market performance, the market simulationengine generating one or more simulated outcomes for each of the annuityproducts in the subset of annuity products; and generating, by a clientcomputing device communicably coupled to the server computing device, agraphical user interface for display to the user via a display device,the graphical user interface including one or more visualrepresentations of each of: the one or more annuity productrecommendations and the one or more simulated outcomes, wherein themachine learning processor and the engines execute the artificialintelligence algorithm to improve the computer-generated models byautomatically assimilating newly-collected input data elements from anexternal data source into the computer-generated models without relyingon manual intervention, and wherein the method further comprisingbuilding, by the machine learning processor, the computer-generatedannuity matching model by training a machine learning algorithmprogrammed on the machine learning processor against a training dataset.
 18. The method of claim 17, wherein the input data set is generatedby retrieving at least a portion of the user information, market indexinformation, and available annuity products from the external datasource.
 19. The method of claim 17, wherein the server computing devicereceives at least a portion of the user information from the user. 20.The method of claim 19, further comprising executing, by the machinelearning processor, a user needs assessment engine to traverse acomputer-generated user insight analysis model using the userinformation, the user needs assessment engine generating one or moreexpected user attributes.
 21. The method of claim 20, wherein the servercomputing device transmits the one or more expected user attributes tothe client computing device for display to the user.
 22. The method ofclaim 21, further comprising receiving, by the server computing device,a response to the one or more expected user attributes from the clientcomputing device and reexecute the user needs assessment engine usingthe received response to adjust one or more of the expected userattributes.
 23. The method of claim 17, wherein the graphical userinterface includes one or more input controls to enable interaction withand manipulation of the one or more visual representations by the userof the client computing device.
 24. The method of claim 17, furthercomprising building, by the machine learning processor, thecomputer-generated annuity prediction performance model by training amachine learning algorithm programmed on the machine learning processorusing the input data set.
 25. The method of claim 17, wherein theproduct characteristics are reference market indices, participationrate, downside protection, commitment term, income benefits, and fees.26. The method of claim 17, wherein the user risk preferences include arisk tolerance.
 27. The method of claim 17, wherein the machine learningprocessor returns the generated one or more annuity productrecommendations to the price optimization engine as input to update thecomputer-generated annuity matching model.
 28. The method of claim 17,wherein the machine learning processor returns the generated one or moresimulated outcomes to the market simulation engine as input to updatethe computer-generated annuity performance prediction model.
 29. Themethod of claim 17, wherein the one or more predictions of marketperformance include at least one prediction of market performancereceived from the client computing device as input by the user.
 30. Themethod of claim 17, wherein the one or more predictions of marketperformance correspond to a performance of one or more market indices.31. The method of claim 17, wherein the one or more simulated outcomesfor each of the annuity products correspond to an expected rate ofreturn for the annuity product.
 32. The method of claim 17 furthercomprising analyzing market conditions, determine risk exposure fromissued annuities, and expected profitability to dynamically priceannuity products.